Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations103076
Missing cells0
Missing cells (%)0.0%
Duplicate rows159
Duplicate rows (%)0.2%
Total size in memory18.1 MiB
Average record size in memory184.0 B

Variable types

Categorical4
Text2
DateTime1
Numeric16

Alerts

Dataset has 159 (0.2%) duplicate rowsDuplicates
Average_Rating is highly overall correlated with Hotel_Name and 5 other fieldsHigh correlation
Breadth is highly overall correlated with DepthHigh correlation
Crawled_date is highly overall correlated with Hotel_Name and 1 other fieldsHigh correlation
Depth is highly overall correlated with BreadthHigh correlation
Hotel_Name is highly overall correlated with Average_Rating and 9 other fieldsHigh correlation
Num_of_Ratings is highly overall correlated with Hotel_Name and 1 other fieldsHigh correlation
cleanliness_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
comfort_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
employee_friendliness_score is highly overall correlated with Average_Rating and 5 other fieldsHigh correlation
facility_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
hotel_grade is highly overall correlated with Hotel_Name and 3 other fieldsHigh correlation
location_score is highly overall correlated with Hotel_NameHigh correlation
value_for_money_score is highly overall correlated with Average_Rating and 6 other fieldsHigh correlation
is_photo is highly imbalanced (71.7%) Imbalance
Crawled_date is highly imbalanced (87.7%) Imbalance
Helpfulness has 93909 (91.1%) zeros Zeros
Deviation.of.star.ratings has 2958 (2.9%) zeros Zeros

Reproduction

Analysis started2025-01-08 14:58:52.936654
Analysis finished2025-01-08 14:59:20.887260
Duration27.95 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Hotel_Name
Categorical

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.4 KiB
blakemore
 
4940
park-plaza-county-hall
 
4929
milleniumgloucester
 
4919
zedwell-trocaderor
 
4909
lancaster-gate
 
4907
Other values (28)
78472 

Length

Max length35
Median length22
Mean length15.898424
Min length3

Characters and Unicode

Total characters1638746
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstudios2let
2nd rowstudios2let
3rd rowstudios2let
4th rowstudios2let
5th rowstudios2let

Common Values

ValueCountFrequency (%)
blakemore 4940
 
4.8%
park-plaza-county-hall 4929
 
4.8%
milleniumgloucester 4919
 
4.8%
zedwell-trocaderor 4909
 
4.8%
lancaster-gate 4907
 
4.8%
thistletower 4888
 
4.7%
stgileshotel 4874
 
4.7%
z-trafalgar 4728
 
4.6%
marlin-waterloo 4334
 
4.2%
nyx-hotel-london-by-leonardo-hotels 3725
 
3.6%
Other values (23) 55923
54.3%

Length

2025-01-08T23:59:20.950829image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
blakemore 4940
 
4.8%
park-plaza-county-hall 4929
 
4.8%
milleniumgloucester 4919
 
4.8%
zedwell-trocaderor 4909
 
4.8%
lancaster-gate 4907
 
4.8%
thistletower 4888
 
4.7%
stgileshotel 4874
 
4.7%
z-trafalgar 4728
 
4.6%
marlin-waterloo 4334
 
4.2%
nyx-hotel-london-by-leonardo-hotels 3725
 
3.6%
Other values (23) 55923
54.3%

Most occurring characters

ValueCountFrequency (%)
e 174901
10.7%
o 161413
 
9.8%
t 158482
 
9.7%
l 150614
 
9.2%
a 125519
 
7.7%
r 116000
 
7.1%
n 95427
 
5.8%
s 87823
 
5.4%
- 84284
 
5.1%
i 72550
 
4.4%
Other values (16) 411733
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1638746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 174901
10.7%
o 161413
 
9.8%
t 158482
 
9.7%
l 150614
 
9.2%
a 125519
 
7.7%
r 116000
 
7.1%
n 95427
 
5.8%
s 87823
 
5.4%
- 84284
 
5.1%
i 72550
 
4.4%
Other values (16) 411733
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1638746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 174901
10.7%
o 161413
 
9.8%
t 158482
 
9.7%
l 150614
 
9.2%
a 125519
 
7.7%
r 116000
 
7.1%
n 95427
 
5.8%
s 87823
 
5.4%
- 84284
 
5.1%
i 72550
 
4.4%
Other values (16) 411733
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1638746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 174901
10.7%
o 161413
 
9.8%
t 158482
 
9.7%
l 150614
 
9.2%
a 125519
 
7.7%
r 116000
 
7.1%
n 95427
 
5.8%
s 87823
 
5.4%
- 84284
 
5.1%
i 72550
 
4.4%
Other values (16) 411733
25.1%
Distinct92513
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:21.220393image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length3534
Median length1868
Mean length208.59817
Min length3

Characters and Unicode

Total characters21501465
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91987 ?
Unique (%)89.2%

Sample

1st rowPerfect location with good connections and shops and pubs
2nd rowThe room had everything you needed Near to amenities, was good room for price just needs little updatingThe bed was so hard it felt like sleeping on a hard floor, you had to make sure you had something on your feet as flooring pinched you feet needs changing
3rd rowConveniently nearby St Pancras, very small but clean and pleasant room first floor with small balcony to street side Interesting areaLuggage service can be improved by offering to lock luggage up instead of it just being put into the hall with all risks on the guests
4th rowReception staffed 24 hours a dayAll good
5th rowVery convenient to Kings Cross and the cityA little dated could do with a lick of paint
ValueCountFrequency (%)
the 212453
 
5.6%
and 145876
 
3.8%
was 122295
 
3.2%
to 97348
 
2.5%
a 90768
 
2.4%
room 73085
 
1.9%
in 64514
 
1.7%
very 53636
 
1.4%
for 50886
 
1.3%
of 49457
 
1.3%
Other values (68127) 2867604
74.9%
2025-01-08T23:59:21.554168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3745873
17.4%
e 2063096
 
9.6%
o 1592601
 
7.4%
t 1541090
 
7.2%
a 1479756
 
6.9%
n 1145021
 
5.3%
r 1048825
 
4.9%
i 1025214
 
4.8%
s 961241
 
4.5%
l 822258
 
3.8%
Other values (58) 6076490
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21501465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3745873
17.4%
e 2063096
 
9.6%
o 1592601
 
7.4%
t 1541090
 
7.2%
a 1479756
 
6.9%
n 1145021
 
5.3%
r 1048825
 
4.9%
i 1025214
 
4.8%
s 961241
 
4.5%
l 822258
 
3.8%
Other values (58) 6076490
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21501465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3745873
17.4%
e 2063096
 
9.6%
o 1592601
 
7.4%
t 1541090
 
7.2%
a 1479756
 
6.9%
n 1145021
 
5.3%
r 1048825
 
4.9%
i 1025214
 
4.8%
s 961241
 
4.5%
l 822258
 
3.8%
Other values (58) 6076490
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21501465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3745873
17.4%
e 2063096
 
9.6%
o 1592601
 
7.4%
t 1541090
 
7.2%
a 1479756
 
6.9%
n 1145021
 
5.3%
r 1048825
 
4.9%
i 1025214
 
4.8%
s 961241
 
4.5%
l 822258
 
3.8%
Other values (58) 6076490
28.3%
Distinct1107
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size805.4 KiB
Minimum2021-12-01 00:00:00
Maximum2024-12-13 00:00:00
2025-01-08T23:59:21.669128image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:21.759310image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Rating
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7423522
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:21.837936image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8768098
Coefficient of variation (CV)0.24240822
Kurtosis2.2077181
Mean7.7423522
Median Absolute Deviation (MAD)1
Skewness-1.3245229
Sum798050.7
Variance3.5224151
MonotonicityNot monotonic
2025-01-08T23:59:21.911657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
8 31625
30.7%
9 19014
18.4%
10 17137
16.6%
7 17064
16.6%
6 7131
 
6.9%
5 4277
 
4.1%
4 2374
 
2.3%
3 1800
 
1.7%
1 1704
 
1.7%
2 878
 
0.9%
Other values (15) 72
 
0.1%
ValueCountFrequency (%)
1 1704
 
1.7%
2 878
 
0.9%
2.5 1
 
< 0.1%
2.9 1
 
< 0.1%
3 1800
1.7%
3.8 1
 
< 0.1%
4 2374
2.3%
4.6 1
 
< 0.1%
5 4277
4.1%
5.4 2
 
< 0.1%
ValueCountFrequency (%)
10 17137
16.6%
9.6 15
 
< 0.1%
9.2 12
 
< 0.1%
9 19014
18.4%
8.8 7
 
< 0.1%
8.3 8
 
< 0.1%
8 31625
30.7%
7.9 7
 
< 0.1%
7.5 5
 
< 0.1%
7.1 3
 
< 0.1%

Average_Rating
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8543851
Minimum7
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:21.980798image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7
Q17.6
median7.8
Q38.2
95-th percentile8.6
Maximum8.7
Range1.7
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.43634279
Coefficient of variation (CV)0.055554036
Kurtosis-0.50902763
Mean7.8543851
Median Absolute Deviation (MAD)0.3
Skewness0.031159136
Sum809598.6
Variance0.19039503
MonotonicityNot monotonic
2025-01-08T23:59:22.051976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7.7 23014
22.3%
7.4 9828
9.5%
7.8 9262
9.0%
8.4 8453
 
8.2%
7.9 7817
 
7.6%
8.3 7485
 
7.3%
8.6 6873
 
6.7%
7 6849
 
6.6%
7.6 5479
 
5.3%
8 5153
 
5.0%
Other values (5) 12863
12.5%
ValueCountFrequency (%)
7 6849
 
6.6%
7.1 1995
 
1.9%
7.4 9828
9.5%
7.5 2157
 
2.1%
7.6 5479
 
5.3%
7.7 23014
22.3%
7.8 9262
9.0%
7.9 7817
 
7.6%
8 5153
 
5.0%
8.1 4334
 
4.2%
ValueCountFrequency (%)
8.7 2562
 
2.5%
8.6 6873
 
6.7%
8.4 8453
 
8.2%
8.3 7485
 
7.3%
8.2 1815
 
1.8%
8.1 4334
 
4.2%
8 5153
 
5.0%
7.9 7817
 
7.6%
7.8 9262
9.0%
7.7 23014
22.3%

Num_of_Ratings
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11857.156
Minimum5613
Maximum39497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:22.129973image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5613
5-th percentile5898
Q17132
median9767
Q313923
95-th percentile20956
Maximum39497
Range33884
Interquartile range (IQR)6791

Descriptive statistics

Standard deviation7266.1052
Coefficient of variation (CV)0.61280339
Kurtosis7.2025689
Mean11857.156
Median Absolute Deviation (MAD)3252
Skewness2.580654
Sum1.2221882 × 109
Variance52796285
MonotonicityNot monotonic
2025-01-08T23:59:22.210609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
12340 4940
 
4.8%
11045 4929
 
4.8%
15320 4919
 
4.8%
39497 4909
 
4.8%
14445 4907
 
4.8%
20956 4888
 
4.7%
14989 4874
 
4.7%
13923 4728
 
4.6%
10695 4334
 
4.2%
9394 3725
 
3.6%
Other values (24) 55923
54.3%
ValueCountFrequency (%)
5613 1884
1.8%
5715 1944
1.9%
5898 1815
1.8%
5932 2188
2.1%
5933 2362
2.3%
6120 2036
2.0%
6248 1735
1.7%
6277 2401
2.3%
6335 2117
2.1%
6404 1975
1.9%
ValueCountFrequency (%)
39497 4909
4.8%
20956 4888
4.7%
15320 4919
4.8%
14989 4874
4.7%
14445 4907
4.8%
13923 4728
4.6%
12641 3492
3.4%
12340 4940
4.8%
11670 3443
3.3%
11045 4929
4.8%

Helpfulness
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10376809
Minimum0
Maximum14
Zeros93909
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:22.282048image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.36788488
Coefficient of variation (CV)3.54526
Kurtosis60.563756
Mean0.10376809
Median Absolute Deviation (MAD)0
Skewness5.3449878
Sum10696
Variance0.13533928
MonotonicityNot monotonic
2025-01-08T23:59:22.349347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 93909
91.1%
1 8004
 
7.8%
2 930
 
0.9%
3 163
 
0.2%
4 38
 
< 0.1%
5 20
 
< 0.1%
6 7
 
< 0.1%
10 2
 
< 0.1%
7 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 93909
91.1%
1 8004
 
7.8%
2 930
 
0.9%
3 163
 
0.2%
4 38
 
< 0.1%
5 20
 
< 0.1%
6 7
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 2
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 7
 
< 0.1%
5 20
 
< 0.1%
4 38
 
< 0.1%
3 163
 
0.2%
2 930
 
0.9%
1 8004
7.8%

is_photo
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.4 KiB
0
97997 
1
 
5079

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103076
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 97997
95.1%
1 5079
 
4.9%

Length

2025-01-08T23:59:22.425991image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T23:59:22.487162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 97997
95.1%
1 5079
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 97997
95.1%
1 5079
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 97997
95.1%
1 5079
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 97997
95.1%
1 5079
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 97997
95.1%
1 5079
 
4.9%
Distinct52445
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:22.653436image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length120
Median length103
Mean length31.436581
Min length1

Characters and Unicode

Total characters3240357
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50032 ?
Unique (%)48.5%

Sample

1st rowExceptional
2nd rowVery good
3rd rowConvenient location
4th rowPeaceful position in an elegant street close to 3 major stations and the Bloomsbury area
5th rowGreat little gem in the city centre
ValueCountFrequency (%)
good 28921
 
5.1%
location 19660
 
3.5%
and 19512
 
3.5%
very 18418
 
3.3%
stay 17046
 
3.0%
great 16452
 
2.9%
a 16229
 
2.9%
for 13650
 
2.4%
the 13547
 
2.4%
hotel 12795
 
2.3%
Other values (10993) 388161
68.8%
2025-01-08T23:59:22.962468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
467085
14.4%
e 303408
 
9.4%
o 284348
 
8.8%
a 245299
 
7.6%
t 237297
 
7.3%
n 182869
 
5.6%
l 157996
 
4.9%
r 156002
 
4.8%
i 150315
 
4.6%
s 114817
 
3.5%
Other values (56) 940921
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3240357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
467085
14.4%
e 303408
 
9.4%
o 284348
 
8.8%
a 245299
 
7.6%
t 237297
 
7.3%
n 182869
 
5.6%
l 157996
 
4.9%
r 156002
 
4.8%
i 150315
 
4.6%
s 114817
 
3.5%
Other values (56) 940921
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3240357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
467085
14.4%
e 303408
 
9.4%
o 284348
 
8.8%
a 245299
 
7.6%
t 237297
 
7.3%
n 182869
 
5.6%
l 157996
 
4.9%
r 156002
 
4.8%
i 150315
 
4.6%
s 114817
 
3.5%
Other values (56) 940921
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3240357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
467085
14.4%
e 303408
 
9.4%
o 284348
 
8.8%
a 245299
 
7.6%
t 237297
 
7.3%
n 182869
 
5.6%
l 157996
 
4.9%
r 156002
 
4.8%
i 150315
 
4.6%
s 114817
 
3.5%
Other values (56) 940921
29.0%

hotel_grade
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.4 KiB
4
50093 
3
38828 
5
7095 
0
 
4909
2
 
2151

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters103076
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
4 50093
48.6%
3 38828
37.7%
5 7095
 
6.9%
0 4909
 
4.8%
2 2151
 
2.1%

Length

2025-01-08T23:59:23.070137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T23:59:23.133792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
4 50093
48.6%
3 38828
37.7%
5 7095
 
6.9%
0 4909
 
4.8%
2 2151
 
2.1%

Most occurring characters

ValueCountFrequency (%)
4 50093
48.6%
3 38828
37.7%
5 7095
 
6.9%
0 4909
 
4.8%
2 2151
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 50093
48.6%
3 38828
37.7%
5 7095
 
6.9%
0 4909
 
4.8%
2 2151
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 50093
48.6%
3 38828
37.7%
5 7095
 
6.9%
0 4909
 
4.8%
2 2151
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 50093
48.6%
3 38828
37.7%
5 7095
 
6.9%
0 4909
 
4.8%
2 2151
 
2.1%

employee_friendliness_score
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5369029
Minimum7.5
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:23.198274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum7.5
5-th percentile8
Q18.3
median8.6
Q38.7
95-th percentile9.1
Maximum9.1
Range1.6
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.36805694
Coefficient of variation (CV)0.043113638
Kurtosis0.57229583
Mean8.5369029
Median Absolute Deviation (MAD)0.2
Skewness-0.6845645
Sum879949.8
Variance0.13546591
MonotonicityNot monotonic
2025-01-08T23:59:23.270091image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8.7 21129
20.5%
8.6 15348
14.9%
8.1 9783
9.5%
8.4 9325
9.0%
9.1 9234
9.0%
8.5 8860
8.6%
9 8479
8.2%
8.3 5600
 
5.4%
8 4919
 
4.8%
8.8 4312
 
4.2%
Other values (2) 6087
 
5.9%
ValueCountFrequency (%)
7.5 3970
 
3.9%
8 4919
 
4.8%
8.1 9783
9.5%
8.2 2117
 
2.1%
8.3 5600
 
5.4%
8.4 9325
9.0%
8.5 8860
8.6%
8.6 15348
14.9%
8.7 21129
20.5%
8.8 4312
 
4.2%
ValueCountFrequency (%)
9.1 9234
9.0%
9 8479
8.2%
8.8 4312
 
4.2%
8.7 21129
20.5%
8.6 15348
14.9%
8.5 8860
8.6%
8.4 9325
9.0%
8.3 5600
 
5.4%
8.2 2117
 
2.1%
8.1 9783
9.5%

facility_score
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8648415
Minimum6.9
Maximum8.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:23.343136image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile6.9
Q17.5
median7.8
Q38.3
95-th percentile8.7
Maximum8.7
Range1.8
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.50545226
Coefficient of variation (CV)0.064267317
Kurtosis-0.80286885
Mean7.8648415
Median Absolute Deviation (MAD)0.3
Skewness0.024449694
Sum810676.4
Variance0.25548198
MonotonicityNot monotonic
2025-01-08T23:59:23.417820image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7.8 12677
12.3%
7.5 12543
12.2%
8.7 11216
10.9%
7.6 9227
9.0%
8.3 8036
 
7.8%
6.9 6849
 
6.6%
8 6724
 
6.5%
7.4 4919
 
4.8%
7.2 4909
 
4.8%
8.4 4728
 
4.6%
Other values (7) 21248
20.6%
ValueCountFrequency (%)
6.9 6849
6.6%
7.2 4909
 
4.8%
7.3 1995
 
1.9%
7.4 4919
 
4.8%
7.5 12543
12.2%
7.6 9227
9.0%
7.7 4136
 
4.0%
7.8 12677
12.3%
7.9 2362
 
2.3%
8 6724
6.5%
ValueCountFrequency (%)
8.7 11216
10.9%
8.6 1944
 
1.9%
8.5 2545
 
2.5%
8.4 4728
 
4.6%
8.3 8036
7.8%
8.2 4334
 
4.2%
8.1 3932
 
3.8%
8 6724
6.5%
7.9 2362
 
2.3%
7.8 12677
12.3%

cleanliness_score
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2579611
Minimum7.3
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:23.490245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.4
Q18
median8.2
Q38.7
95-th percentile8.8
Maximum9.1
Range1.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.4396936
Coefficient of variation (CV)0.053244813
Kurtosis-0.38757973
Mean8.2579611
Median Absolute Deviation (MAD)0.3
Skewness-0.20031132
Sum851197.6
Variance0.19333046
MonotonicityNot monotonic
2025-01-08T23:59:23.562641image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
8.7 14579
14.1%
8.2 11627
11.3%
8.8 11199
10.9%
7.9 10388
10.1%
8.1 9453
9.2%
8.3 8890
8.6%
8 8831
8.6%
8.4 7723
7.5%
7.8 4919
 
4.8%
7.3 4874
 
4.7%
Other values (4) 10593
10.3%
ValueCountFrequency (%)
7.3 4874
4.7%
7.4 1975
 
1.9%
7.5 1995
 
1.9%
7.8 4919
4.8%
7.9 10388
10.1%
8 8831
8.6%
8.1 9453
9.2%
8.2 11627
11.3%
8.3 8890
8.6%
8.4 7723
7.5%
ValueCountFrequency (%)
9.1 4506
 
4.4%
8.8 11199
10.9%
8.7 14579
14.1%
8.5 2117
 
2.1%
8.4 7723
7.5%
8.3 8890
8.6%
8.2 11627
11.3%
8.1 9453
9.2%
8 8831
8.6%
7.9 10388
10.1%

comfort_score
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2565427
Minimum7.3
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:23.633540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.3
Q18
median8.2
Q38.7
95-th percentile8.9
Maximum9.1
Range1.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.46494936
Coefficient of variation (CV)0.056312838
Kurtosis-0.56190603
Mean8.2565427
Median Absolute Deviation (MAD)0.3
Skewness-0.14082429
Sum851051.4
Variance0.21617791
MonotonicityNot monotonic
2025-01-08T23:59:23.709823image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
8 13299
12.9%
7.9 11985
11.6%
8.2 10957
10.6%
8.8 10581
10.3%
8.1 9713
9.4%
8.9 8654
8.4%
8.3 6862
6.7%
7.3 6849
6.6%
8.5 5503
 
5.3%
8.7 4728
 
4.6%
Other values (6) 13945
13.5%
ValueCountFrequency (%)
7.3 6849
6.6%
7.4 1995
 
1.9%
7.8 3443
 
3.3%
7.9 11985
11.6%
8 13299
12.9%
8.1 9713
9.4%
8.2 10957
10.6%
8.3 6862
6.7%
8.4 1884
 
1.8%
8.5 5503
5.3%
ValueCountFrequency (%)
9.1 2562
 
2.5%
9 1944
 
1.9%
8.9 8654
8.4%
8.8 10581
10.3%
8.7 4728
4.6%
8.6 2117
 
2.1%
8.5 5503
5.3%
8.4 1884
 
1.8%
8.3 6862
6.7%
8.2 10957
10.6%

value_for_money_score
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7126276
Minimum7
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:23.783499image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.3
Q17.4
median7.7
Q37.9
95-th percentile8.2
Maximum8.3
Range1.3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.32440552
Coefficient of variation (CV)0.042061608
Kurtosis-0.69888089
Mean7.7126276
Median Absolute Deviation (MAD)0.2
Skewness-0.17966144
Sum794986.8
Variance0.10523894
MonotonicityNot monotonic
2025-01-08T23:59:23.857130image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.9 24163
23.4%
7.4 13982
13.6%
7.5 10839
10.5%
8.1 10398
10.1%
7.6 9941
9.6%
7.7 9019
 
8.7%
7.3 7076
 
6.9%
7 4874
 
4.7%
8.2 4728
 
4.6%
8 4297
 
4.2%
ValueCountFrequency (%)
7 4874
 
4.7%
7.3 7076
 
6.9%
7.4 13982
13.6%
7.5 10839
10.5%
7.6 9941
9.6%
7.7 9019
 
8.7%
7.9 24163
23.4%
8 4297
 
4.2%
8.1 10398
10.1%
8.2 4728
 
4.6%
ValueCountFrequency (%)
8.3 3759
 
3.6%
8.2 4728
 
4.6%
8.1 10398
10.1%
8 4297
 
4.2%
7.9 24163
23.4%
7.7 9019
 
8.7%
7.6 9941
9.6%
7.5 10839
10.5%
7.4 13982
13.6%
7.3 7076
 
6.9%

location_score
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1732751
Minimum8.2
Maximum9.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:23.923271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile8.6
Q19
median9.1
Q39.4
95-th percentile9.6
Maximum9.7
Range1.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.30954624
Coefficient of variation (CV)0.033744354
Kurtosis0.42117248
Mean9.1732751
Median Absolute Deviation (MAD)0.2
Skewness-0.49357467
Sum945544.5
Variance0.095818878
MonotonicityNot monotonic
2025-01-08T23:59:23.995603image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8.9 16389
15.9%
9.1 14855
14.4%
9 13837
13.4%
9.3 11502
11.2%
9.4 11122
10.8%
9.5 9817
9.5%
9.6 7471
7.2%
9.2 5923
 
5.7%
8.6 5437
 
5.3%
9.7 4728
 
4.6%
ValueCountFrequency (%)
8.2 1995
 
1.9%
8.6 5437
 
5.3%
8.9 16389
15.9%
9 13837
13.4%
9.1 14855
14.4%
9.2 5923
 
5.7%
9.3 11502
11.2%
9.4 11122
10.8%
9.5 9817
9.5%
9.6 7471
7.2%
ValueCountFrequency (%)
9.7 4728
 
4.6%
9.6 7471
7.2%
9.5 9817
9.5%
9.4 11122
10.8%
9.3 11502
11.2%
9.2 5923
 
5.7%
9.1 14855
14.4%
9 13837
13.4%
8.9 16389
15.9%
8.6 5437
 
5.3%

Crawled_date
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size805.4 KiB
2024-12-02
101341 
2024-12-16
 
1735

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1030760
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-12-02
2nd row2024-12-02
3rd row2024-12-02
4th row2024-12-02
5th row2024-12-02

Common Values

ValueCountFrequency (%)
2024-12-02 101341
98.3%
2024-12-16 1735
 
1.7%

Length

2025-01-08T23:59:24.162849image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-08T23:59:24.223082image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2024-12-02 101341
98.3%
2024-12-16 1735
 
1.7%

Most occurring characters

ValueCountFrequency (%)
2 410569
39.8%
- 206152
20.0%
0 204417
19.8%
1 104811
 
10.2%
4 103076
 
10.0%
6 1735
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1030760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 410569
39.8%
- 206152
20.0%
0 204417
19.8%
1 104811
 
10.2%
4 103076
 
10.0%
6 1735
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1030760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 410569
39.8%
- 206152
20.0%
0 204417
19.8%
1 104811
 
10.2%
4 103076
 
10.0%
6 1735
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1030760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 410569
39.8%
- 206152
20.0%
0 204417
19.8%
1 104811
 
10.2%
4 103076
 
10.0%
6 1735
 
0.2%

title_word_count
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5156195
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:24.288468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q38
95-th percentile17
Maximum31
Range30
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.1106655
Coefficient of variation (CV)0.92658051
Kurtosis1.7625261
Mean5.5156195
Median Absolute Deviation (MAD)3
Skewness1.4345584
Sum568528
Variance26.118902
MonotonicityNot monotonic
2025-01-08T23:59:24.367930image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 25775
25.0%
2 17582
17.1%
4 7344
 
7.1%
5 6841
 
6.6%
7 6627
 
6.4%
6 6568
 
6.4%
3 5139
 
5.0%
8 4402
 
4.3%
9 3810
 
3.7%
10 3159
 
3.1%
Other values (20) 15829
15.4%
ValueCountFrequency (%)
1 25775
25.0%
2 17582
17.1%
3 5139
 
5.0%
4 7344
 
7.1%
5 6841
 
6.6%
6 6568
 
6.4%
7 6627
 
6.4%
8 4402
 
4.3%
9 3810
 
3.7%
10 3159
 
3.1%
ValueCountFrequency (%)
31 1
 
< 0.1%
29 2
 
< 0.1%
28 6
 
< 0.1%
27 27
 
< 0.1%
26 48
 
< 0.1%
25 130
 
0.1%
24 250
 
0.2%
23 718
0.7%
22 393
0.4%
21 550
0.5%

text_word_count
Real number (ℝ)

Distinct419
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.370959
Minimum1
Maximum666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:24.451691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q111
median24
Q349
95-th percentile113
Maximum666
Range665
Interquartile range (IQR)38

Descriptive statistics

Standard deviation40.749444
Coefficient of variation (CV)1.090404
Kurtosis15.877065
Mean37.370959
Median Absolute Deviation (MAD)16
Skewness3.0554809
Sum3852049
Variance1660.5172
MonotonicityNot monotonic
2025-01-08T23:59:24.543195image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 2997
 
2.9%
5 2990
 
2.9%
6 2906
 
2.8%
11 2812
 
2.7%
9 2693
 
2.6%
8 2564
 
2.5%
12 2548
 
2.5%
4 2546
 
2.5%
10 2452
 
2.4%
14 2384
 
2.3%
Other values (409) 76184
73.9%
ValueCountFrequency (%)
1 668
 
0.6%
2 1751
1.7%
3 1909
1.9%
4 2546
2.5%
5 2990
2.9%
6 2906
2.8%
7 2997
2.9%
8 2564
2.5%
9 2693
2.6%
10 2452
2.4%
ValueCountFrequency (%)
666 1
< 0.1%
568 1
< 0.1%
527 1
< 0.1%
510 1
< 0.1%
503 1
< 0.1%
493 1
< 0.1%
491 1
< 0.1%
479 1
< 0.1%
471 1
< 0.1%
469 1
< 0.1%

Deviation.of.star.ratings
Real number (ℝ)

Zeros 

Distinct84
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.297985
Minimum0
Maximum7.7
Zeros2958
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:24.635050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.4
median1
Q31.7
95-th percentile3.9
Maximum7.7
Range7.7
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.268132
Coefficient of variation (CV)0.97700054
Kurtosis5.4931347
Mean1.297985
Median Absolute Deviation (MAD)0.6
Skewness2.124388
Sum133791.1
Variance1.6081588
MonotonicityNot monotonic
2025-01-08T23:59:24.731259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 9564
 
9.3%
0.4 8601
 
8.3%
0.3 7017
 
6.8%
1.3 6405
 
6.2%
1.4 6368
 
6.2%
1 4527
 
4.4%
0.7 4209
 
4.1%
0.1 4164
 
4.0%
1.6 3218
 
3.1%
0.3 3193
 
3.1%
Other values (74) 45810
44.4%
ValueCountFrequency (%)
0 2958
 
2.9%
0.1 4164
4.0%
0.2 600
 
0.6%
0.2 2649
 
2.6%
0.3 7017
6.8%
0.3 3193
 
3.1%
0.4 8601
8.3%
0.5 930
 
0.9%
0.6 9564
9.3%
0.6 2
 
< 0.1%
ValueCountFrequency (%)
7.7 17
 
< 0.1%
7.6 18
 
< 0.1%
7.4 99
 
0.1%
7.3 54
 
0.1%
7.2 9
 
< 0.1%
7.1 69
 
0.1%
7 90
 
0.1%
6.9 120
 
0.1%
6.8 162
 
0.2%
6.7 487
0.5%

Time_lapsed
Real number (ℝ)

Distinct1100
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean504.78416
Minimum-2
Maximum1097
Zeros70
Zeros (%)0.1%
Negative28
Negative (%)< 0.1%
Memory size805.4 KiB
2025-01-08T23:59:24.826243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile33
Q1197
median504
Q3804
95-th percentile1018
Maximum1097
Range1099
Interquartile range (IQR)607

Descriptive statistics

Standard deviation330.64121
Coefficient of variation (CV)0.65501502
Kurtosis-1.2597732
Mean504.78416
Median Absolute Deviation (MAD)303
Skewness0.064456178
Sum52031132
Variance109323.61
MonotonicityNot monotonic
2025-01-08T23:59:24.914162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 1138
 
1.1%
33 935
 
0.9%
34 905
 
0.9%
41 900
 
0.9%
39 874
 
0.8%
21 751
 
0.7%
35 704
 
0.7%
42 517
 
0.5%
38 494
 
0.5%
40 446
 
0.4%
Other values (1090) 95412
92.6%
ValueCountFrequency (%)
-2 2
 
< 0.1%
-1 26
 
< 0.1%
0 70
0.1%
1 70
0.1%
2 101
0.1%
3 98
0.1%
4 81
0.1%
5 57
0.1%
6 79
0.1%
7 118
0.1%
ValueCountFrequency (%)
1097 3
 
< 0.1%
1096 41
 
< 0.1%
1095 53
 
0.1%
1094 57
 
0.1%
1093 80
0.1%
1092 152
0.1%
1091 89
0.1%
1090 80
0.1%
1089 78
0.1%
1088 70
0.1%

Depth
Real number (ℝ)

High correlation 

Distinct91515
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6673556
Minimum0.012601162
Maximum4.1688739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:25.002301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.012601162
5-th percentile0.24036735
Q13.9315144
median4.1088943
Q34.1469427
95-th percentile4.1630048
Maximum4.1688739
Range4.1562727
Interquartile range (IQR)0.21542832

Descriptive statistics

Standard deviation1.0835643
Coefficient of variation (CV)0.29546202
Kurtosis5.224727
Mean3.6673556
Median Absolute Deviation (MAD)0.04869006
Skewness-2.5800111
Sum378016.35
Variance1.1741116
MonotonicityNot monotonic
2025-01-08T23:59:25.091205image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7336058717 100
 
0.1%
0.07129944682 100
 
0.1%
0.327605505 100
 
0.1%
3.436142792 100
 
0.1%
0.2729918326 100
 
0.1%
0.08027615348 100
 
0.1%
0.09504141834 100
 
0.1%
0.1977737147 100
 
0.1%
0.080685688 100
 
0.1%
0.3522561707 100
 
0.1%
Other values (91505) 102076
99.0%
ValueCountFrequency (%)
0.01260116246 1
< 0.1%
0.01914650691 1
< 0.1%
0.02194744686 1
< 0.1%
0.02298841269 1
< 0.1%
0.02486463467 1
< 0.1%
0.025432713 1
< 0.1%
0.02629962357 1
< 0.1%
0.02654172296 1
< 0.1%
0.02950573643 1
< 0.1%
0.02974145401 1
< 0.1%
ValueCountFrequency (%)
4.168873903 1
< 0.1%
4.168804929 1
< 0.1%
4.168781023 1
< 0.1%
4.168646412 1
< 0.1%
4.16864324 1
< 0.1%
4.168638399 1
< 0.1%
4.16857928 1
< 0.1%
4.168541557 1
< 0.1%
4.168530101 1
< 0.1%
4.16852448 1
< 0.1%

Breadth
Real number (ℝ)

High correlation 

Distinct91515
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44844776
Minimum0.00022905809
Maximum5.8055472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size805.4 KiB
2025-01-08T23:59:25.177511image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.00022905809
5-th percentile0.005378859
Q10.019625352
median0.049508995
Q30.239119
95-th percentile3.2482945
Maximum5.8055472
Range5.8053182
Interquartile range (IQR)0.21949365

Descriptive statistics

Standard deviation0.95668542
Coefficient of variation (CV)2.1333263
Kurtosis5.1430972
Mean0.44844776
Median Absolute Deviation (MAD)0.039350876
Skewness2.5298934
Sum46224.202
Variance0.91524699
MonotonicityNot monotonic
2025-01-08T23:59:25.268520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.811815413 100
 
0.1%
3.272018845 100
 
0.1%
3.021920156 100
 
0.1%
2.051197399 100
 
0.1%
3.136129587 100
 
0.1%
3.265073295 100
 
0.1%
3.248287686 100
 
0.1%
3.145693353 100
 
0.1%
3.260204296 100
 
0.1%
3.086971247 100
 
0.1%
Other values (91505) 102076
99.0%
ValueCountFrequency (%)
0.000229058089 1
 
< 0.1%
0.0002640175533 1
 
< 0.1%
0.0002717905476 1
 
< 0.1%
0.0002735136346 4
 
< 0.1%
0.0002749332064 1
 
< 0.1%
0.0002860301209 2
 
< 0.1%
0.0002949128143 1
 
< 0.1%
0.0002969371781 12
< 0.1%
0.0002971830134 2
 
< 0.1%
0.0003024245461 2
 
< 0.1%
ValueCountFrequency (%)
5.805547214 1
 
< 0.1%
5.403308331 7
 
< 0.1%
5.097892004 1
 
< 0.1%
5.068863578 1
 
< 0.1%
5.045583762 1
 
< 0.1%
5.044899191 1
 
< 0.1%
5.018399874 1
 
< 0.1%
4.968003829 24
< 0.1%
4.955974295 1
 
< 0.1%
4.938898305 23
< 0.1%

Interactions

2025-01-08T23:59:18.890509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:01.246100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.514509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.800651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.969106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:06.178339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.324338image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.465131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.686068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.822108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.952595image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.176975image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:14.328159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.495189image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.729319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.818486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.952763image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:01.330878image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.586779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.868168image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.033001image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:06.244040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.389076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.531312image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.751692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.886341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.017520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-08T23:59:15.561377image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.790720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.878590image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:19.024354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:01.519942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.771547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.948556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.106582image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:06.319291image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.462731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.603254image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.823722image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-08T23:59:12.094496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-08T23:59:14.475529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.637065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.862219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.948502image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:19.095886image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:01.597822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.845036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.022958image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.180403image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:06.394574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.534755image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.677758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.896583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.032923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.167262image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.388458image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:14.549575image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.710521image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.931391image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.016295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:19.164207image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:01.681081image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.919911image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.099625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-08T23:59:07.607089image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-08T23:59:11.104730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.240626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.466034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:14.620588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.782295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.998787image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-08T23:59:02.997801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-08T23:59:05.323396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-08T23:59:07.680179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.824474image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.041905image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.177170image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.312982image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.542646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:14.700956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.858315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.070078image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.153785image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:19.307490image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:01.829564image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.072219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.249404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.395599image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:06.613547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.752388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.897434image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.113525image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.250277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.387791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.617022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:14.784251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.932190image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.139959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.223340image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-01-08T23:59:01.902812image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.145903image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.323224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.468995image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:06.687851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.824896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.970217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.186112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.322678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.460625image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.690012image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:14.862558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.008303image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.211080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.294032image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:19.451430image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:01.977068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.222061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.398455image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.542117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:06.761178image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.898804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.042827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.256999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.396546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.534668image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.763967image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:14.935650image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.082181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.281099image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.363127image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:19.522471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.046304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.295562image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.469856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.613810image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:06.833002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.969691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.115729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.328700image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.465268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.605698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.834937image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.006877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.155227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.350290image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.431591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:19.593019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.115188image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.374217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.545718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.685900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:06.905100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.044459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.188893image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.401386image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.537344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.678482image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.909299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.080856image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.229850image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.419697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.500000image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:19.661002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.181917image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.445572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.615497image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.757115image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:06.975815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.114371image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.260005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.470686image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.607698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.747840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.977779image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.149743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.380408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.487847image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.564933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:19.728245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.248746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.519536image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.687606image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.826470image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.045959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.186999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.329786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.542182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.676793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.897886image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:14.048388image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.219434image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.451841image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.554601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.631928image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:19.799257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.322607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.595070image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.762672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.900464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.120954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.261471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.405329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.616588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.750659image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:12.971256image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:14.121919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.293173image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.524617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.625971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.701004image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:19.945068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.386813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.663417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.831678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:05.966848image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.188576image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.330537image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.551354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.685829image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.817454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.039439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:14.189546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.361648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.593808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.689578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.765769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:20.006432image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:02.449362image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:03.729972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:04.897776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:06.030277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:07.253496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:08.395441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:09.616934image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:10.750175image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:11.883111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:13.103746image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:14.254572image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:15.425688image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:16.658801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:17.750589image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-08T23:59:18.825169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-01-08T23:59:25.345697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Average_RatingBreadthCrawled_dateDepthDeviation.of.star.ratingsHelpfulnessHotel_NameNum_of_RatingsRatingTime_lapsedcleanliness_scorecomfort_scoreemployee_friendliness_scorefacility_scorehotel_gradeis_photolocation_scoretext_word_counttitle_word_countvalue_for_money_score
Average_Rating1.0000.1030.194-0.078-0.023-0.0311.000-0.3020.305-0.0290.9220.8890.8450.9480.4920.0870.179-0.0480.0260.720
Breadth0.1031.0000.035-0.9230.0560.0270.227-0.045-0.063-0.3260.0530.0680.0090.1150.1350.063-0.1070.4250.1130.031
Crawled_date0.1940.0351.0000.0640.0560.0001.0000.1760.0160.0370.2580.2610.2870.2960.1680.0000.2590.0220.0570.627
Depth-0.078-0.9230.0641.000-0.079-0.0270.2120.0370.0730.311-0.033-0.0430.030-0.0940.1310.0500.136-0.408-0.103-0.039
Deviation.of.star.ratings-0.0230.0560.056-0.0791.000-0.0350.191-0.120-0.0300.0480.0090.0020.0350.0060.1300.050-0.0680.040-0.0750.008
Helpfulness-0.0310.0270.000-0.027-0.0351.0000.032-0.002-0.0770.048-0.018-0.017-0.037-0.0240.0100.021-0.0070.1280.013-0.015
Hotel_Name1.0000.2271.0000.2120.1910.0321.0001.0000.1670.1691.0001.0001.0001.0001.0000.1451.0000.0430.0661.000
Num_of_Ratings-0.302-0.0450.1760.037-0.120-0.0021.0001.000-0.078-0.049-0.359-0.243-0.304-0.3180.5920.1110.3420.0130.024-0.363
Rating0.305-0.0630.0160.073-0.030-0.0770.167-0.0781.000-0.0230.2880.2870.2720.2960.1290.0810.082-0.200-0.0050.212
Time_lapsed-0.029-0.3260.0370.3110.0480.0480.169-0.049-0.0231.0000.0010.0090.036-0.0310.0990.0260.028-0.051-0.0480.058
cleanliness_score0.9220.0530.258-0.0330.009-0.0181.000-0.3590.2880.0011.0000.9610.8510.9510.5310.0830.113-0.0450.0180.757
comfort_score0.8890.0680.261-0.0430.002-0.0171.000-0.2430.2870.0090.9611.0000.8210.9430.4690.0610.131-0.0390.0230.664
employee_friendliness_score0.8450.0090.2870.0300.035-0.0371.000-0.3040.2720.0360.8510.8211.0000.8210.3940.0980.144-0.0580.0210.706
facility_score0.9480.1150.296-0.0940.006-0.0241.000-0.3180.296-0.0310.9510.9430.8211.0000.6540.0890.096-0.0420.0170.704
hotel_grade0.4920.1350.1680.1310.1300.0101.0000.5920.1290.0990.5310.4690.3940.6541.0000.0540.4120.0180.0240.332
is_photo0.0870.0630.0000.0500.0500.0210.1450.1110.0810.0260.0830.0610.0980.0890.0541.0000.0720.0880.1090.076
location_score0.179-0.1070.2590.136-0.068-0.0071.0000.3420.0820.0280.1130.1310.1440.0960.4120.0721.000-0.0090.0410.011
text_word_count-0.0480.4250.022-0.4080.0400.1280.0430.013-0.200-0.051-0.045-0.039-0.058-0.0420.0180.088-0.0091.0000.212-0.040
title_word_count0.0260.1130.057-0.103-0.0750.0130.0660.024-0.005-0.0480.0180.0230.0210.0170.0240.1090.0410.2121.0000.016
value_for_money_score0.7200.0310.627-0.0390.008-0.0151.000-0.3630.2120.0580.7570.6640.7060.7040.3320.0760.011-0.0400.0161.000

Missing values

2025-01-08T23:59:20.131245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-08T23:59:20.454072image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Hotel_NameReview_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_word_counttext_word_countDeviation.of.star.ratingsTime_lapsedDepthBreadth
0studios2letPerfect location with good connections and shops and pubs2024-05-0110.07.61167000Exceptional38.37.57.97.87.69.32024-12-02192.42154.1607580.014384
1studios2letThe room had everything you needed Near to amenities, was good room for price just needs little updatingThe bed was so hard it felt like sleeping on a hard floor, you had to make sure you had something on your feet as flooring pinched you feet needs changing2024-12-028.07.61167000Very good38.37.57.97.87.69.32024-12-022480.404.0287480.106661
2studios2letConveniently nearby St Pancras, very small but clean and pleasant room first floor with small balcony to street side Interesting areaLuggage service can be improved by offering to lock luggage up instead of it just being put into the hall with all risks on the guests2024-12-018.07.61167000Convenient location38.37.57.97.87.69.32024-12-022460.414.1456470.015296
3studios2letReception staffed 24 hours a dayAll good2024-12-019.07.61167000Peaceful position in an elegant street close to 3 major stations and the Bloomsbury area38.37.57.97.87.69.32024-12-021571.414.1528220.018400
4studios2letVery convenient to Kings Cross and the cityA little dated could do with a lick of paint2024-11-308.07.61167000Great little gem in the city centre38.37.57.97.87.69.32024-12-027170.423.9817520.114333
5studios2letLocated in a quiet area but close to Kings Cross station so getting around was easy Several little pubs nearby for dining and some good coffee shops tooThere is no lift so dragging a heavy suitcase up and down stairs was challenging We had booked a room with terrace but the outdoor space was really minuscule not what we had expected from the photos2024-11-307.07.61167000Convenient, quiet location38.37.57.97.87.69.32024-12-023650.624.0508820.052436
6studios2letIts spacious, good value and so very quiet for LondonYou sometimes have to wriggle the loo flusher to stop it running and running2024-11-309.07.61167000Superb38.37.57.97.87.69.32024-12-021231.424.1514210.017526
7studios2letLocationLot of stairs bad knee2024-11-299.07.61167000Ideal location for travelling round38.37.57.97.87.69.32024-12-02551.434.1431190.012692
8studios2letLocation was great, so near the stationWe were on the top floor, six flights of stairs and no lift\r\nHeating was on 247 full temperature and no means of reducing it2024-11-297.07.61167000Perfect location,38.37.57.97.87.69.32024-12-022310.633.9498410.160883
9studios2letThe location which is excellent for public transport and local dining \r\nFriendly staffed reception where we could leave our travel bags all day after checking outThe climb up 3 flights of stairs was exhausting but it was our choice\r\nIt was a small room and the kitchen facilities were very sparse but we didnt need them2024-11-288.07.61167000Ideal accommodation for a short stay in London near St Pancreas station38.37.57.97.87.69.32024-12-0212570.444.1197460.060710
Hotel_NameReview_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_word_counttext_word_countDeviation.of.star.ratingsTime_lapsedDepthBreadth
103066montanahotelPerfect Location2023-09-0210.07.8624800Lovely staff39.07.78.28.28.09.42024-12-16222.24710.2844123.274274
103067montanahotelIt was good enoughDelayed check in, cracked basin in bathroom, water pressure poor Everything else was fine2023-07-116.07.8624800Not a bad place to stay if its where you need to be39.07.78.28.28.09.42024-12-1613171.85240.0330473.061864
103068montanahotellocationhot room, shower didnt drain, broken sink2023-07-015.07.8624800Great location and generally clean spot but the place is a bit a dated and the basement room was damp, hot and a bit mus39.07.78.28.28.09.42024-12-162572.85340.0825534.014645
103069montanahotelGood to have teacoffee and a fridge in the roomThe building is beautiful but the interior decor leaves a lot to be desired The hotel is Indian in styleredgoldfaded wallpaper and threadbare carpetsthe room was OK but again needed updating We were in the basement with no viewonly rubbish out of the window There was no hot breakfast so only cereals, fruit and pastriesbut it was in a pleasant locationnear the tube and shopspubs etconly a short walk to the Natural History museum2023-04-256.07.8624800Lovely building with quite a grand entrancelet down by the interiorfine for overnight stay39.07.78.28.28.09.42024-12-1614831.86010.0229884.066392
103070montanahotellocationwater pressure was non existent\r\ndespite several request to address the problem2023-03-253.07.8624800while the staff was nice They did very little to remedy the lack of shower and hot water problem we had39.07.78.28.28.09.42024-12-1622124.86320.0829784.013796
103071montanahotelConvenient and classy The staff are excellent people, and Light of India is a fantastic restaurant I would certainly stay againNA2022-12-2810.07.8624800Highly recommend this little gem situated in my favourite part of town39.07.78.28.28.09.42024-12-1612212.27190.0676424.028017
103072montanahotellovely atmosphere, extremely friendly and helpful staff2022-07-0110.07.8624800Perfect location for our visit to the Royal Albert Hall and the Natural History Museum would39.07.78.28.28.09.42024-12-161672.28990.0880963.441931
103073montanahotelIt was a single room, a little small but it was fine for 1 person, it had everything I needed2022-06-2810.07.8624801The staff were very friendly and helpful The position was perfect for sightseeing39.07.78.28.28.09.42024-12-1613202.29020.0839234.012930
103074montanahotelVery clean and well maintainedThe rooms are very nice and comfortable with staffs professionalismThe food are delicious,nice breakfast,lunch ,dinner and the cocktails are exceptionalNotting much just that theres no parking2022-02-1610.07.8624801Myself and my wife really enjoy our stay at this hotel,we love the service and all the staffs are amazingLooking forwar39.07.78.28.28.09.42024-12-1621302.210340.0248653.068829
103075montanahotelThe staff were very friendly and helpful Especially Kampas The hotel was very clean and the fact that they had a wonderful Indian restaurant as part of it was amazing Best Vindaloo everShower a tad small but adequate xx2022-02-0610.07.8624801Loved every minute we will be back Xxx39.07.78.28.28.09.42024-12-168392.210440.0354884.055666

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Most frequently occurring

Hotel_NameReview_TextPosted_DateRatingAverage_RatingNum_of_RatingsHelpfulnessis_photoreview_titlehotel_gradeemployee_friendliness_scorefacility_scorecleanliness_scorecomfort_scorevalue_for_money_scorelocation_scoreCrawled_datetitle_word_counttext_word_countDeviation.of.star.ratingsTime_lapsedDepthBreadth# duplicates
0blakemoreBreakfast was lovely, room was good and a nice showerIt was a little bit noisy but to be expected in city location2024-10-219.08.31234000nice stay48.78.38.78.88.18.92024-12-022220.7420.2729923.136130100
5blakemoreClean, nice big room, comfy bed2024-10-249.08.31234000Wish wed stayed longer48.78.38.78.88.18.92024-12-02460.7393.4361432.051197100
8blakemoreGreat location Very friendly and helpful concierge Comfortable room and bed Decent breakfastDidnt like the position of room lower ground floor Most of the staff at reception werent very friendly excluding one guy but cant remember his name In general they just chatted among themselves and didnt greet guests except when checking them in2024-10-247.08.31234000Comfortable hotel in a good location48.78.38.78.88.18.92024-12-026551.3390.0825143.261759100
13blakemoreHandy and good valueThe twin beds had no space between them2024-10-228.08.31234000Enjoyable, modern and clean48.78.38.78.88.18.92024-12-024110.3413.4003042.059872100
14blakemoreI liked afternoon drinks in the restaurant2024-10-228.08.31234000Very good48.78.38.78.88.18.92024-12-02270.3410.8184752.830759100
17blakemoreLocation was great, easy to get there Room was just as the pictures, no surprise, comfortable bed, clean room Nice staffNA2024-10-219.08.31234000The hotel is in amazing place just a 2 min walk from Hyde park Neighbourhood was nice, quiet and clean Staff was nice,48.78.38.78.88.18.92024-12-0223210.7420.2676783.137761100
20blakemoreLovely outside area \r\nGreat size bed really comfortableNo mini bar in room fridge was empty No room service after eleven or didnt seem to be as no one answered the phone2024-10-258.08.31234000Comfortable and clean very good location48.78.38.78.88.18.92024-12-027320.3380.1304453.229430100
23blakemoreThe breakfast was a bit bland but I guess thats a cultural differenceMore variety in the breakfast or perhaps a different breakfast menu for different days2024-10-248.08.31234000Excellent overall Very friendly staff48.78.38.78.88.18.92024-12-025260.3390.1523823.215152100
26blakemoreThe property was wellmaintained and very clean the room was as described and well appointed We had daily housekeeping and dining room and bar were comfortable and relaxingOur room wouldnt cool when we first arrived but that was quickly corrected by staff Other than that I have nothing bad to say about the hotel2024-10-249.08.31234000We would definitely stay here again and would certainly recommend it to friends and family traveling to London48.78.38.78.88.18.92024-12-0218540.7390.1039763.247377100
29blakemoreThe wait for breakfast table can sometimes be very long, some food items and cups run out and the staff manning the breakfast room is on the rude side2024-10-228.08.31234000Great location but some small improvements needed48.78.38.78.88.18.92024-12-027290.3410.1541303.213937100